Computing with words (CWW) relies on linguistic representation of knowledge that is processed by operating at the semantical level defined through fuzzy sets. Linguistic representation of knowledge is a major issue when fuzzy rule based models are acquired from data by some form of empirical learning. Indeed, these models are often requested to exhibit interpretability, which is normally evalua...

The concepts of Soft Computing introduced by Lotfi A. Zadeh in 1991 has integrated different methodologies and approaches, as: fuzzy set theory, fuzzy logic, approximate reasoning, linguistic expression of knowledge, probabilistic reasoning, and others for solving problems of complex systems in the way similar to human perception, recognition and solving problem methods. Linguistic fuzzy modell...

Optimization of machining parameters is very important and the main goal in every machining process. Surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. In this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. This approach has both the learning capability of neural network and li...

To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the op...

Intuitionistic fuzzy linguistic numbers (IFLNs) are an extension of the linguistic variables and the intuitionistic fuzzy sets, which are proposed by Wang and Li (Wang J.Q., Li J.J. (2009) The multi-criteria group decision making method based on multi-granularity intuitionistic two semantics. Science & Technology Information (33), 8-9.). In this paper, we introduced some operational laws of IFL...

Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest. Subgroup discovery algorithms aim at discovering individual rules, which must be re...

Multi-criteria decision-making methods support decision makers in all stages of the decision-making process by providing useful data. However, criteria are not always certain as uncertainty is a feature of the real world. Multicriteria decision-making methods under uncertainty and fuzzy systems are accepted as suitable techniques in conflicting problems that cannot be represented by numerical v...

More and more digitized art images are accumulated and expanded in our daily life and techniques are needed to be established on how to organize and retrieve them. Though content-based image retrieval (CBIR)made great progress, current low-level visual information based retrieval technology in CBIR does not allow users to search images by high-level semantics for art image retrieval. We propose...

This paper presents the basic concepts of stability in fuzzy linguistic models. Theauthors have proposed a criterion for BIBO stability analysis of fuzzy linguistic modelsassociated to linear time invariant systems [25]-[28]. This paper presents the basic concepts ofstability in the general nonlinear and linear systems. This stability analysis method is verifiedusing a benchmark system analysis.

Scientific and technological papers play a fundamental role in the scientific and technological innovation of countries. The quality control of scientific and technological articles is vital to the journals and management of personnel. This paper investigates multiple attribute decision-making problems with the application of hesitant fuzzy uncertain linguistic information. Motivated by the ide...